louvain算法matlab实现，不定义函数模块

以下是Louvain算法的Matlab实现，不需要定义函数模块。代码中使用了一个名为"network"的矩阵来表示网络，该矩阵的每一行表示一条边，第一列是源节点，第二列是目标节点，第三列是边的权重。

% 读取网络数据
network = dlmread('network.txt');

% 获取节点数和边数
num_nodes = max(max(network(:, 1)), max(network(:, 2)));
num_edges = size(network, 1);

% 初始化每个节点的社区标签为它自己
node_labels = 1:num_nodes;

% 初始化每个社区的节点集合
communities = cell(1,num_nodes);
for i=1:num_nodes
    communities{i} = i;
end

delta_modularity = 1;
while delta_modularity > 0
    delta_modularity = 0;
    
    % 对每个节点进行遍历
    for i=1:num_nodes
        neighbors = find(network(:,1) == i | network(:,2) == i); % 获取与当前节点相邻的边
        
        if isempty(neighbors)
            continue;
        end
        
        % 计算当前节点在当前社区中的模块度
        current_community = node_labels(i);
        current_in_degree = sum(network(neighbors, 3));
        current_out_degree = sum(network(node_labels(neighbors) ~= current_community, 3));
        current_modularity = current_in_degree - current_out_degree;
        
        % 在相邻的社区中寻找最大的模块度增量
        max_modularity_increase = -inf;
        new_community = current_community;
        for j=1:length(neighbors)
            neighbor = neighbors(j);
            neighbor_node = network(neighbor, 1);
            if neighbor_node == i
                neighbor_node = network(neighbor, 2);
            end
            
            neighbor_community = node_labels(neighbor_node);
            if neighbor_community == current_community
                continue;
            end
            
            neighbor_in_degree = sum(network(node_labels == neighbor_community & (network(:,1) == neighbor_node | network(:,2) == neighbor_node), 3));
            neighbor_out_degree = sum(network(node_labels == neighbor_community & ~(network(:,1) == neighbor_node | network(:,2) == neighbor_node), 3));
            modularity_increase = neighbor_in_degree - neighbor_out_degree;
            
            if modularity_increase > max_modularity_increase
                max_modularity_increase = modularity_increase;
                new_community = neighbor_community;
            end
        end
        
        % 如果找到了更优的社区，则更新当前节点的社区标签
        if max_modularity_increase > 0
            node_labels(i) = new_community;
            communities{current_community} = communities{current_community}(communities{current_community} ~= i);
            communities{new_community} = [communities{new_community} i];
            delta_modularity = delta_modularity + max_modularity_increase;
        end
    end
end

% 输出最终的社区划分
for i=1:num_nodes
    fprintf('%d belongs to community %d\n', i, node_labels(i));
end

需要注意的是，该实现中使用的是贪心策略，可能会陷入局部最优解。为了解决这个问题，可以多次运行算法并随机初始化每个节点的社区标签，然后选择模块度最大的一次划分结果作为最终结果。